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    <abstract>Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extraction if required by the above decision; (3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available.</abstract>
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%0 Conference Proceedings
%T ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
%A Zhang, Xiao
%A Huang, Heyan
%A Chi, Zewen
%A Mao, Xian-Ling
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F zhang-etal-2022-et5
%X Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extraction if required by the above decision; (3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available.
%U https://aclanthology.org/2022.coling-1.47
%P 570-579

Markdown (Informal)

[ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension](https://aclanthology.org/2022.coling-1.47) (Zhang et al., COLING 2022)

• ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (Zhang et al., COLING 2022)

ACL

• Xiao Zhang, Heyan Huang, Zewen Chi, and Xian-Ling Mao. 2022. ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension. In Proceedings of the 29th International Conference on Computational Linguistics, pages 570–579, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.